Using Viral Dynamics to Connect Clinical Markers of Disease Progression to Sequence Evolution for HIV-1
HIV-1 remains a global health challenge, with over 35 million people infected. The high rates of turnover and evolutionary adaptability exhibited by HIV-1 pose a particular challenge to the use of antiretrovirals, as well as the development of a vaccine. Our focus is to understand the dynamics of two of the most commonly tracked clinical markers of an HIV-1 infection: CD4+ T cells/mm3 (CD4 count) and HIV-1 RNA/ml (viral load). We are developing a dynamic mathematical model of HIV-1 infection that uses equilibration, adaptation, and inheritance to model the initial infection by a founding virus as well as successive generations of viral lineages. We have calibrated our model to match viral load set points and rates of CD4 decline from 91 HIV-infected individuals studied longitudinally during early stages of the disease. We plan to incorporate sustained tissue damage and antiretroviral treatment to study how drug resistance could develop.